import cartopy.crs as ccrs
import intake
import uxarray as ux

cat_url = "https://digital-earths-global-hackathon.github.io/catalog/catalog.yaml"
cat = intake.open_catalog(cat_url).NCAR
cat

NCAR:
  args:
    path: https://digital-earths-global-hackathon.github.io/catalog/NCAR/catalog.yaml
  description: catalog as visible from NCAR
  driver: intake.catalog.local.YAMLFileCatalog
  metadata:
    catalog_dir: https://digital-earths-global-hackathon.github.io/catalog

# cat.walk(depth=-1)

list(cat)

['CERES_EBAF',
 'ERA5',
 'IR_IMERG',
 'JRA3Q',
 'MERRA2',
 'casesm2_10km_nocumulus',
 'icon_d3hp003',
 'icon_d3hp003aug',
 'icon_d3hp003feb',
 'icon_ngc4008',
 'ifs_tco3999-ng5_deepoff',
 'ifs_tco3999-ng5_rcbmf',
 'ifs_tco3999-ng5_rcbmf_cf',
 'mpas_dyamond1',
 'mpas_dyamond2',
 'mpas_dyamond3',
 'nicam_gl11',
 'scream-dkrz',
 'scream2D_hrly',
 'scream_lnd',
 'scream_ne120',
 'tracking-d3hp003',
 'um_Africa_km4p4_RAL3P3_n1280_GAL9_nest',
 'um_CTC_km4p4_RAL3P3_n1280_GAL9_nest',
 'um_SAmer_km4p4_RAL3P3_n1280_GAL9_nest',
 'um_SEA_km4p4_RAL3P3_n1280_GAL9_nest',
 'um_glm_n1280_CoMA9_TBv1p2',
 'um_glm_n1280_GAL9',
 'um_glm_n2560_RAL3p3',
 'wrf_conus',
 'wrf_samerica']

ds_mpas2 = cat.mpas_dyamond2(zoom=3).to_dask()
ds_mpas2

/glade/u/apps/opt/conda/envs/2025-digital-earths-global-hackathon/lib/python3.12/site-packages/intake_xarray/base.py:21: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  'dims': dict(self._ds.dims),

<xarray.Dataset> Size: 357MB
Dimensions:                 (time: 329, cell: 768, nVertLevels: 55,
                             nSoilLevels: 4, nVertLevelsP1: 56)
Coordinates:
  * cell                    (cell) float64 6kB nan 1.0 2.0 ... 765.0 766.0 767.0
  * nSoilLevels             (nSoilLevels) float64 32B nan 1.0 2.0 3.0
  * nVertLevels             (nVertLevels) float64 440B nan 1.0 2.0 ... 53.0 54.0
  * nVertLevelsP1           (nVertLevelsP1) float64 448B nan 1.0 ... 54.0 55.0
  * time                    (time) datetime64[ns] 3kB 2020-01-20 ... 2020-03-01
Data variables: (12/19)
    air_temperature         (time, cell, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
    h_oml                   (time, cell) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
    hu_oml                  (time, cell) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
    hv_oml                  (time, cell) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
    pressure                (time, cell, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
    relhum                  (time, cell, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
    ...                      ...
    tslb                    (time, cell, nSoilLevels) float32 4MB dask.array<chunksize=(1, 768, 4), meta=np.ndarray>
    uReconstructMeridional  (time, cell, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
    uReconstructZonal       (time, cell, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
    vegfra                  (time, cell) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
    w                       (time, cell, nVertLevelsP1) float32 57MB dask.array<chunksize=(1, 768, 56), meta=np.ndarray>
    xice                    (time, cell) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • time: 329
  • cell: 768
  • nVertLevels: 55
  • nSoilLevels: 4
  • nVertLevelsP1: 56
• Coordinates: (5)
  • cell
    (cell)
    float64
    nan 1.0 2.0 ... 765.0 766.0 767.0

    long_name :

    HEALPix pixel index

    units :

    1

    nside :

    8

    array([ nan,   1.,   2., ..., 765., 766., 767.], shape=(768,))

  • nSoilLevels
    (nSoilLevels)
    float64
    nan 1.0 2.0 3.0

    long_name :

    nSoilLevels

    array([nan,  1.,  2.,  3.])

  • nVertLevels
    (nVertLevels)
    float64
    nan 1.0 2.0 3.0 ... 52.0 53.0 54.0

    long_name :

    nVertLevels

    array([nan,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13.,
           14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.,
           28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41.,
           42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54.])

  • nVertLevelsP1
    (nVertLevelsP1)
    float64
    nan 1.0 2.0 3.0 ... 53.0 54.0 55.0

    long_name :

    nVertLevelsP1

    array([nan,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13.,
           14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.,
           28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41.,
           42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55.])

  • time
    (time)
    datetime64[ns]
    2020-01-20 ... 2020-03-01

    long_name :

    time

    axis :

    T

    reference_date :

    2000-01-01 00:00:00

    array(['2020-01-20T00:00:00.000000000', '2020-01-20T03:00:00.000000000',
           '2020-01-20T06:00:00.000000000', ..., '2020-02-29T18:00:00.000000000',
           '2020-02-29T21:00:00.000000000', '2020-03-01T00:00:00.000000000'],
          shape=(329,), dtype='datetime64[ns]')

• Data variables: (19)
  • air_temperature
    (time, cell, nVertLevels)
    float32
    dask.array<chunksize=(1, 768, 55), meta=np.ndarray>

    units :

    K

    long_name :

    air_temperature

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 53.01 MiB 165.00 kiB Shape (329, 768, 55) (1, 768, 55) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • h_oml
    (time, cell)
    float32
    dask.array<chunksize=(1, 768), meta=np.ndarray>

    units :

    m

    long_name :

    ocean mixed layer depth

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • hu_oml
    (time, cell)
    float32
    dask.array<chunksize=(1, 768), meta=np.ndarray>

    units :

    m^2 s^{-1}

    long_name :

    ocean mixed layer integrated u (zonal velocity)

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • hv_oml
    (time, cell)
    float32
    dask.array<chunksize=(1, 768), meta=np.ndarray>

    units :

    m^2 s^{-1}

    long_name :

    ocean mixed layer integrated v (meridional velocity)

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • pressure
    (time, cell, nVertLevels)
    float32
    dask.array<chunksize=(1, 768, 55), meta=np.ndarray>

    units :

    Pa

    long_name :

    Pressure

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 53.01 MiB 165.00 kiB Shape (329, 768, 55) (1, 768, 55) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • relhum
    (time, cell, nVertLevels)
    float32
    dask.array<chunksize=(1, 768, 55), meta=np.ndarray>

    units :

    percent

    long_name :

    Relative humidity

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 53.01 MiB 165.00 kiB Shape (329, 768, 55) (1, 768, 55) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sh2o
    (time, cell, nSoilLevels)
    float32
    dask.array<chunksize=(1, 768, 4), meta=np.ndarray>

    units :

    m3 m^{-3}

    long_name :

    soil equivalent liquid water

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 3.86 MiB 12.00 kiB Shape (329, 768, 4) (1, 768, 4) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • skintemp
    (time, cell)
    float32
    dask.array<chunksize=(1, 768), meta=np.ndarray>

    units :

    K

    long_name :

    ground or water surface temperature

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • smois
    (time, cell, nSoilLevels)
    float32
    dask.array<chunksize=(1, 768, 4), meta=np.ndarray>

    units :

    m3 m^{-3}

    long_name :

    soil moisture

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 3.86 MiB 12.00 kiB Shape (329, 768, 4) (1, 768, 4) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • snow
    (time, cell)
    float32
    dask.array<chunksize=(1, 768), meta=np.ndarray>

    units :

    kg m^{-2}

    long_name :

    snow water equivalent

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • snowh
    (time, cell)
    float32
    dask.array<chunksize=(1, 768), meta=np.ndarray>

    units :

    m

    long_name :

    physical snow depth

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sst
    (time, cell)
    float32
    dask.array<chunksize=(1, 768), meta=np.ndarray>

    units :

    K

    long_name :

    sea-surface temperature

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • t_oml
    (time, cell)
    float32
    dask.array<chunksize=(1, 768), meta=np.ndarray>

    units :

    K

    long_name :

    ocean mixed layer temperature

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tslb
    (time, cell, nSoilLevels)
    float32
    dask.array<chunksize=(1, 768, 4), meta=np.ndarray>

    units :

    K

    long_name :

    soil layer temperature

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 3.86 MiB 12.00 kiB Shape (329, 768, 4) (1, 768, 4) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • uReconstructMeridional
    (time, cell, nVertLevels)
    float32
    dask.array<chunksize=(1, 768, 55), meta=np.ndarray>

    units :

    m s^{-1}

    long_name :

    Meridional component of reconstructed horizontal velocity at cell centers

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 53.01 MiB 165.00 kiB Shape (329, 768, 55) (1, 768, 55) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • uReconstructZonal
    (time, cell, nVertLevels)
    float32
    dask.array<chunksize=(1, 768, 55), meta=np.ndarray>

    units :

    m s^{-1}

    long_name :

    Zonal component of reconstructed horizontal velocity at cell centers

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 53.01 MiB 165.00 kiB Shape (329, 768, 55) (1, 768, 55) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vegfra
    (time, cell)
    float32
    dask.array<chunksize=(1, 768), meta=np.ndarray>

    units :

    unitless

    long_name :

    vegetation fraction

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • w
    (time, cell, nVertLevelsP1)
    float32
    dask.array<chunksize=(1, 768, 56), meta=np.ndarray>

    units :

    m s^{-1}

    long_name :

    Vertical velocity at vertical cell faces

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 53.98 MiB 168.00 kiB Shape (329, 768, 56) (1, 768, 56) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • xice
    (time, cell)
    float32
    dask.array<chunksize=(1, 768), meta=np.ndarray>

    units :

    unitless

    long_name :

    fractional area coverage of sea-ice

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (5)
  • cell
    PandasIndex

    PandasIndex(Index([  nan,   1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,   8.0,   9.0,
           ...
           758.0, 759.0, 760.0, 761.0, 762.0, 763.0, 764.0, 765.0, 766.0, 767.0],
          dtype='float64', name='cell', length=768))

  • nSoilLevels
    PandasIndex

    PandasIndex(Index([nan, 1.0, 2.0, 3.0], dtype='float64', name='nSoilLevels'))

  • nVertLevels
    PandasIndex

    PandasIndex(Index([ nan,  1.0,  2.0,  3.0,  4.0,  5.0,  6.0,  7.0,  8.0,  9.0, 10.0, 11.0,
           12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0,
           24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0,
           36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0,
           48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0],
          dtype='float64', name='nVertLevels'))

  • nVertLevelsP1
    PandasIndex

    PandasIndex(Index([ nan,  1.0,  2.0,  3.0,  4.0,  5.0,  6.0,  7.0,  8.0,  9.0, 10.0, 11.0,
           12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0,
           24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0,
           36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0,
           48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0],
          dtype='float64', name='nVertLevelsP1'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-01-20 00:00:00', '2020-01-20 03:00:00',
                   '2020-01-20 06:00:00', '2020-01-20 09:00:00',
                   '2020-01-20 12:00:00', '2020-01-20 15:00:00',
                   '2020-01-20 18:00:00', '2020-01-20 21:00:00',
                   '2020-01-21 00:00:00', '2020-01-21 03:00:00',
                   ...
                   '2020-02-28 21:00:00', '2020-02-29 00:00:00',
                   '2020-02-29 03:00:00', '2020-02-29 06:00:00',
                   '2020-02-29 09:00:00', '2020-02-29 12:00:00',
                   '2020-02-29 15:00:00', '2020-02-29 18:00:00',
                   '2020-02-29 21:00:00', '2020-03-01 00:00:00'],
                  dtype='datetime64[ns]', name='time', length=329, freq=None))

• Attributes: (0)

Let us plot snow

# Open a HEALPix UX array
uxds = ux.UxDataset.from_healpix(ds_mpas2)
uxds

<xarray.UxDataset> Size: 357MB
Dimensions:                 (time: 329, n_face: 768, nVertLevels: 55,
                             nSoilLevels: 4, nVertLevelsP1: 56)
Coordinates:
    cell                    (n_face) float64 6kB nan 1.0 2.0 ... 766.0 767.0
  * nSoilLevels             (nSoilLevels) float64 32B nan 1.0 2.0 3.0
  * nVertLevels             (nVertLevels) float64 440B nan 1.0 2.0 ... 53.0 54.0
  * nVertLevelsP1           (nVertLevelsP1) float64 448B nan 1.0 ... 54.0 55.0
  * time                    (time) datetime64[ns] 3kB 2020-01-20 ... 2020-03-01
Dimensions without coordinates: n_face
Data variables: (12/19)
    air_temperature         (time, n_face, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
    h_oml                   (time, n_face) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
    hu_oml                  (time, n_face) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
    hv_oml                  (time, n_face) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
    pressure                (time, n_face, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
    relhum                  (time, n_face, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
    ...                      ...
    tslb                    (time, n_face, nSoilLevels) float32 4MB dask.array<chunksize=(1, 768, 4), meta=np.ndarray>
    uReconstructMeridional  (time, n_face, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
    uReconstructZonal       (time, n_face, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
    vegfra                  (time, n_face) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
    w                       (time, n_face, nVertLevelsP1) float32 57MB dask.array<chunksize=(1, 768, 56), meta=np.ndarray>
    xice                    (time, n_face) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>

<xarray.UxDataset> Size: 357MB
Dimensions:                 (time: 329, n_face: 768, nVertLevels: 55,
                             nSoilLevels: 4, nVertLevelsP1: 56)
Coordinates:
    cell                    (n_face) float64 6kB nan 1.0 2.0 ... 766.0 767.0
  * nSoilLevels             (nSoilLevels) float64 32B nan 1.0 2.0 3.0
  * nVertLevels             (nVertLevels) float64 440B nan 1.0 2.0 ... 53.0 54.0
  * nVertLevelsP1           (nVertLevelsP1) float64 448B nan 1.0 ... 54.0 55.0
  * time                    (time) datetime64[ns] 3kB 2020-01-20 ... 2020-03-01
Dimensions without coordinates: n_face
Data variables: (12/19)
    air_temperature         (time, n_face, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
    h_oml                   (time, n_face) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
    hu_oml                  (time, n_face) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
    hv_oml                  (time, n_face) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
    pressure                (time, n_face, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
    relhum                  (time, n_face, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
    ...                      ...
    tslb                    (time, n_face, nSoilLevels) float32 4MB dask.array<chunksize=(1, 768, 4), meta=np.ndarray>
    uReconstructMeridional  (time, n_face, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
    uReconstructZonal       (time, n_face, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
    vegfra                  (time, n_face) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
    w                       (time, n_face, nVertLevelsP1) float32 57MB dask.array<chunksize=(1, 768, 56), meta=np.ndarray>
    xice                    (time, n_face) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>

uxarray.UxDataset

• Dimensions:
  • time: 329
  • n_face: 768
  • nVertLevels: 55
  • nSoilLevels: 4
  • nVertLevelsP1: 56
• Coordinates: (5)
  • cell
    (n_face)
    float64
    nan 1.0 2.0 ... 765.0 766.0 767.0

    long_name :

    HEALPix pixel index

    units :

    1

    nside :

    8

    array([ nan,   1.,   2., ..., 765., 766., 767.], shape=(768,))

  • nSoilLevels
    (nSoilLevels)
    float64
    nan 1.0 2.0 3.0

    long_name :

    nSoilLevels

    array([nan,  1.,  2.,  3.])

  • nVertLevels
    (nVertLevels)
    float64
    nan 1.0 2.0 3.0 ... 52.0 53.0 54.0

    long_name :

    nVertLevels

    array([nan,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13.,
           14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.,
           28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41.,
           42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54.])

  • nVertLevelsP1
    (nVertLevelsP1)
    float64
    nan 1.0 2.0 3.0 ... 53.0 54.0 55.0

    long_name :

    nVertLevelsP1

    array([nan,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13.,
           14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.,
           28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41.,
           42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55.])

  • time
    (time)
    datetime64[ns]
    2020-01-20 ... 2020-03-01

    long_name :

    time

    axis :

    T

    reference_date :

    2000-01-01 00:00:00

    array(['2020-01-20T00:00:00.000000000', '2020-01-20T03:00:00.000000000',
           '2020-01-20T06:00:00.000000000', ..., '2020-02-29T18:00:00.000000000',
           '2020-02-29T21:00:00.000000000', '2020-03-01T00:00:00.000000000'],
          shape=(329,), dtype='datetime64[ns]')

• Data variables: (19)
  • air_temperature
    (time, n_face, nVertLevels)
    float32
    dask.array<chunksize=(1, 768, 55), meta=np.ndarray>

    units :

    K

    long_name :

    air_temperature

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 53.01 MiB 165.00 kiB Shape (329, 768, 55) (1, 768, 55) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • h_oml
    (time, n_face)
    float32
    dask.array<chunksize=(1, 768), meta=np.ndarray>

    units :

    m

    long_name :

    ocean mixed layer depth

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • hu_oml
    (time, n_face)
    float32
    dask.array<chunksize=(1, 768), meta=np.ndarray>

    units :

    m^2 s^{-1}

    long_name :

    ocean mixed layer integrated u (zonal velocity)

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • hv_oml
    (time, n_face)
    float32
    dask.array<chunksize=(1, 768), meta=np.ndarray>

    units :

    m^2 s^{-1}

    long_name :

    ocean mixed layer integrated v (meridional velocity)

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • pressure
    (time, n_face, nVertLevels)
    float32
    dask.array<chunksize=(1, 768, 55), meta=np.ndarray>

    units :

    Pa

    long_name :

    Pressure

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 53.01 MiB 165.00 kiB Shape (329, 768, 55) (1, 768, 55) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • relhum
    (time, n_face, nVertLevels)
    float32
    dask.array<chunksize=(1, 768, 55), meta=np.ndarray>

    units :

    percent

    long_name :

    Relative humidity

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 53.01 MiB 165.00 kiB Shape (329, 768, 55) (1, 768, 55) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sh2o
    (time, n_face, nSoilLevels)
    float32
    dask.array<chunksize=(1, 768, 4), meta=np.ndarray>

    units :

    m3 m^{-3}

    long_name :

    soil equivalent liquid water

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 3.86 MiB 12.00 kiB Shape (329, 768, 4) (1, 768, 4) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • skintemp
    (time, n_face)
    float32
    dask.array<chunksize=(1, 768), meta=np.ndarray>

    units :

    K

    long_name :

    ground or water surface temperature

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • smois
    (time, n_face, nSoilLevels)
    float32
    dask.array<chunksize=(1, 768, 4), meta=np.ndarray>

    units :

    m3 m^{-3}

    long_name :

    soil moisture

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 3.86 MiB 12.00 kiB Shape (329, 768, 4) (1, 768, 4) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • snow
    (time, n_face)
    float32
    dask.array<chunksize=(1, 768), meta=np.ndarray>

    units :

    kg m^{-2}

    long_name :

    snow water equivalent

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • snowh
    (time, n_face)
    float32
    dask.array<chunksize=(1, 768), meta=np.ndarray>

    units :

    m

    long_name :

    physical snow depth

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sst
    (time, n_face)
    float32
    dask.array<chunksize=(1, 768), meta=np.ndarray>

    units :

    K

    long_name :

    sea-surface temperature

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • t_oml
    (time, n_face)
    float32
    dask.array<chunksize=(1, 768), meta=np.ndarray>

    units :

    K

    long_name :

    ocean mixed layer temperature

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tslb
    (time, n_face, nSoilLevels)
    float32
    dask.array<chunksize=(1, 768, 4), meta=np.ndarray>

    units :

    K

    long_name :

    soil layer temperature

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 3.86 MiB 12.00 kiB Shape (329, 768, 4) (1, 768, 4) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • uReconstructMeridional
    (time, n_face, nVertLevels)
    float32
    dask.array<chunksize=(1, 768, 55), meta=np.ndarray>

    units :

    m s^{-1}

    long_name :

    Meridional component of reconstructed horizontal velocity at cell centers

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 53.01 MiB 165.00 kiB Shape (329, 768, 55) (1, 768, 55) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • uReconstructZonal
    (time, n_face, nVertLevels)
    float32
    dask.array<chunksize=(1, 768, 55), meta=np.ndarray>

    units :

    m s^{-1}

    long_name :

    Zonal component of reconstructed horizontal velocity at cell centers

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 53.01 MiB 165.00 kiB Shape (329, 768, 55) (1, 768, 55) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vegfra
    (time, n_face)
    float32
    dask.array<chunksize=(1, 768), meta=np.ndarray>

    units :

    unitless

    long_name :

    vegetation fraction

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • w
    (time, n_face, nVertLevelsP1)
    float32
    dask.array<chunksize=(1, 768, 56), meta=np.ndarray>

    units :

    m s^{-1}

    long_name :

    Vertical velocity at vertical cell faces

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 53.98 MiB 168.00 kiB Shape (329, 768, 56) (1, 768, 56) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

  • xice
    (time, n_face)
    float32
    dask.array<chunksize=(1, 768), meta=np.ndarray>

    units :

    unitless

    long_name :

    fractional area coverage of sea-ice

    healpix_nside :

    8

    healpix_dim_name :

    cell

    original_mpas_spatial_dim :

    nCells

    Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (4)
  • nSoilLevels
    PandasIndex

    PandasIndex(Index([nan, 1.0, 2.0, 3.0], dtype='float64', name='nSoilLevels'))

  • nVertLevels
    PandasIndex

    PandasIndex(Index([ nan,  1.0,  2.0,  3.0,  4.0,  5.0,  6.0,  7.0,  8.0,  9.0, 10.0, 11.0,
           12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0,
           24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0,
           36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0,
           48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0],
          dtype='float64', name='nVertLevels'))

  • nVertLevelsP1
    PandasIndex

    PandasIndex(Index([ nan,  1.0,  2.0,  3.0,  4.0,  5.0,  6.0,  7.0,  8.0,  9.0, 10.0, 11.0,
           12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0,
           24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0,
           36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0,
           48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0],
          dtype='float64', name='nVertLevelsP1'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-01-20 00:00:00', '2020-01-20 03:00:00',
                   '2020-01-20 06:00:00', '2020-01-20 09:00:00',
                   '2020-01-20 12:00:00', '2020-01-20 15:00:00',
                   '2020-01-20 18:00:00', '2020-01-20 21:00:00',
                   '2020-01-21 00:00:00', '2020-01-21 03:00:00',
                   ...
                   '2020-02-28 21:00:00', '2020-02-29 00:00:00',
                   '2020-02-29 03:00:00', '2020-02-29 06:00:00',
                   '2020-02-29 09:00:00', '2020-02-29 12:00:00',
                   '2020-02-29 15:00:00', '2020-02-29 18:00:00',
                   '2020-02-29 21:00:00', '2020-03-01 00:00:00'],
                  dtype='datetime64[ns]', name='time', length=329, freq=None))

• Attributes: (0)

Show Grid Information

<uxarray.Grid>
Original Grid Type: HEALPix
Grid Dimensions:
  * n_face: 768
Grid Coordinates (Spherical):
  * face_lon: (768,)
  * face_lat: (768,)
Grid Coordinates (Cartesian):
Grid Connectivity Variables:
Grid Descriptor Variables:

uxarray.UxDataset.uxgrid

• Dimensions:
  • n_face: 768
• Spherical Coordinates: (2)
  • face_lon
    (n_face)
    float64
    45.0 50.62 39.38 ... -50.62 -45.0

    standard_name :

    longitude

    long name :

    Longitude of the center of each face

    units :

    degrees_east

    array([  45.        ,   50.625     ,   39.375     ,   45.        ,
             56.25      ,   61.875     ,   50.625     ,   56.25      ,
             33.75      ,   39.375     ,   28.125     ,   33.75      ,
             45.        ,   50.625     ,   39.375     ,   45.        ,
             67.5       ,   73.125     ,   61.875     ,   67.5       ,
             78.75      ,   84.375     ,   73.125     ,   83.57142857,
             56.25      ,   61.875     ,   50.625     ,   57.85714286,
             70.71428571,   82.5       ,   67.5       ,   81.        ,
             22.5       ,   28.125     ,   16.875     ,   22.5       ,
             33.75      ,   39.375     ,   28.125     ,   32.14285714,
             11.25      ,   16.875     ,    5.625     ,    6.42857143,
             19.28571429,   22.5       ,    7.5       ,    9.        ,
             45.        ,   52.5       ,   37.5       ,   45.        ,
             63.        ,   78.75      ,   56.25      ,   75.        ,
             27.        ,   33.75      ,   11.25      ,   15.        ,
             45.        ,   67.5       ,   22.5       ,   45.        ,
            135.        ,  140.625     ,  129.375     ,  135.        ,
            146.25      ,  151.875     ,  140.625     ,  146.25      ,
            123.75      ,  129.375     ,  118.125     ,  123.75      ,
            135.        ,  140.625     ,  129.375     ,  135.        ,
    ...
           -135.        , -129.375     , -140.625     , -135.        ,
           -123.75      , -118.125     , -129.375     , -123.75      ,
           -146.25      , -140.625     , -151.875     , -146.25      ,
           -135.        , -129.375     , -140.625     , -135.        ,
            -45.        ,  -22.5       ,  -67.5       ,  -45.        ,
            -15.        ,  -11.25      ,  -33.75      ,  -27.        ,
            -75.        ,  -56.25      ,  -78.75      ,  -63.        ,
            -45.        ,  -37.5       ,  -52.5       ,  -45.        ,
             -9.        ,   -7.5       ,  -22.5       ,  -19.28571429,
             -6.42857143,   -5.625     ,  -16.875     ,  -11.25      ,
            -32.14285714,  -28.125     ,  -39.375     ,  -33.75      ,
            -22.5       ,  -16.875     ,  -28.125     ,  -22.5       ,
            -81.        ,  -67.5       ,  -82.5       ,  -70.71428571,
            -57.85714286,  -50.625     ,  -61.875     ,  -56.25      ,
            -83.57142857,  -73.125     ,  -84.375     ,  -78.75      ,
            -67.5       ,  -61.875     ,  -73.125     ,  -67.5       ,
            -45.        ,  -39.375     ,  -50.625     ,  -45.        ,
            -33.75      ,  -28.125     ,  -39.375     ,  -33.75      ,
            -56.25      ,  -50.625     ,  -61.875     ,  -56.25      ,
            -45.        ,  -39.375     ,  -50.625     ,  -45.        ])

  • face_lat
    (n_face)
    float64
    4.78 9.594 9.594 ... -9.594 -4.78

    standard_name :

    latitude

    long name :

    Latitude of the center of each face

    units :

    degrees_north

    array([  4.78019185,   9.59406823,   9.59406823,  14.47751219,
            14.47751219,  19.47122063,  19.47122063,  24.62431835,
            14.47751219,  19.47122063,  19.47122063,  24.62431835,
            24.62431835,  30.        ,  30.        ,  35.68533471,
            24.62431835,  30.        ,  30.        ,  35.68533471,
            35.68533471,  41.8103149 ,  41.8103149 ,  48.14120779,
            35.68533471,  41.8103149 ,  41.8103149 ,  48.14120779,
            48.14120779,  54.3409123 ,  54.3409123 ,  60.43443884,
            24.62431835,  30.        ,  30.        ,  35.68533471,
            35.68533471,  41.8103149 ,  41.8103149 ,  48.14120779,
            35.68533471,  41.8103149 ,  41.8103149 ,  48.14120779,
            48.14120779,  54.3409123 ,  54.3409123 ,  60.43443884,
            48.14120779,  54.3409123 ,  54.3409123 ,  60.43443884,
            60.43443884,  66.44353569,  66.44353569,  72.38756093,
            60.43443884,  66.44353569,  66.44353569,  72.38756093,
            72.38756093,  78.28414761,  78.28414761,  84.14973294,
             4.78019185,   9.59406823,   9.59406823,  14.47751219,
            14.47751219,  19.47122063,  19.47122063,  24.62431835,
            14.47751219,  19.47122063,  19.47122063,  24.62431835,
            24.62431835,  30.        ,  30.        ,  35.68533471,
    ...
           -35.68533471, -30.        , -30.        , -24.62431835,
           -24.62431835, -19.47122063, -19.47122063, -14.47751219,
           -24.62431835, -19.47122063, -19.47122063, -14.47751219,
           -14.47751219,  -9.59406823,  -9.59406823,  -4.78019185,
           -84.14973294, -78.28414761, -78.28414761, -72.38756093,
           -72.38756093, -66.44353569, -66.44353569, -60.43443884,
           -72.38756093, -66.44353569, -66.44353569, -60.43443884,
           -60.43443884, -54.3409123 , -54.3409123 , -48.14120779,
           -60.43443884, -54.3409123 , -54.3409123 , -48.14120779,
           -48.14120779, -41.8103149 , -41.8103149 , -35.68533471,
           -48.14120779, -41.8103149 , -41.8103149 , -35.68533471,
           -35.68533471, -30.        , -30.        , -24.62431835,
           -60.43443884, -54.3409123 , -54.3409123 , -48.14120779,
           -48.14120779, -41.8103149 , -41.8103149 , -35.68533471,
           -48.14120779, -41.8103149 , -41.8103149 , -35.68533471,
           -35.68533471, -30.        , -30.        , -24.62431835,
           -35.68533471, -30.        , -30.        , -24.62431835,
           -24.62431835, -19.47122063, -19.47122063, -14.47751219,
           -24.62431835, -19.47122063, -19.47122063, -14.47751219,
           -14.47751219,  -9.59406823,  -9.59406823,  -4.78019185])

• Cartesian Coordinates: (0)
• Connectivity: (0)
• Descriptors: (0)
• Attributes: (5)

  zoom :

  3

  n_side :

  8

  n_pix :

  768

  nest :

  True

  source_grid_spec :

  HEALPix

ux_snow = uxds['snow']
ux_snow

<xarray.UxDataArray 'snow' (time: 329, n_face: 768)> Size: 1MB
dask.array<open_dataset-snow, shape=(329, 768), dtype=float32, chunksize=(1, 768), chunktype=numpy.ndarray>
Coordinates:
    cell     (n_face) float64 6kB nan 1.0 2.0 3.0 ... 764.0 765.0 766.0 767.0
  * time     (time) datetime64[ns] 3kB 2020-01-20 ... 2020-03-01
Dimensions without coordinates: n_face
Attributes:
    units:                      kg m^{-2}
    long_name:                  snow water equivalent
    healpix_nside:              8
    healpix_dim_name:           cell
    original_mpas_spatial_dim:  nCells

<xarray.UxDataArray 'snow' (time: 329, n_face: 768)> Size: 1MB
dask.array<open_dataset-snow, shape=(329, 768), dtype=float32, chunksize=(1, 768), chunktype=numpy.ndarray>
Coordinates:
    cell     (n_face) float64 6kB nan 1.0 2.0 3.0 ... 764.0 765.0 766.0 767.0
  * time     (time) datetime64[ns] 3kB 2020-01-20 ... 2020-03-01
Dimensions without coordinates: n_face
Attributes:
    units:                      kg m^{-2}
    long_name:                  snow water equivalent
    healpix_nside:              8
    healpix_dim_name:           cell
    original_mpas_spatial_dim:  nCells

uxarray.UxDataArray

'snow'

• time: 329
• n_face: 768

• dask.array<chunksize=(1, 768), meta=np.ndarray>

  Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray

• Coordinates: (2)
  • cell
    (n_face)
    float64
    nan 1.0 2.0 ... 765.0 766.0 767.0

    long_name :

    HEALPix pixel index

    units :

    1

    nside :

    8

    array([ nan,   1.,   2., ..., 765., 766., 767.], shape=(768,))

  • time
    (time)
    datetime64[ns]
    2020-01-20 ... 2020-03-01

    long_name :

    time

    axis :

    T

    reference_date :

    2000-01-01 00:00:00

    array(['2020-01-20T00:00:00.000000000', '2020-01-20T03:00:00.000000000',
           '2020-01-20T06:00:00.000000000', ..., '2020-02-29T18:00:00.000000000',
           '2020-02-29T21:00:00.000000000', '2020-03-01T00:00:00.000000000'],
          shape=(329,), dtype='datetime64[ns]')

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-01-20 00:00:00', '2020-01-20 03:00:00',
                   '2020-01-20 06:00:00', '2020-01-20 09:00:00',
                   '2020-01-20 12:00:00', '2020-01-20 15:00:00',
                   '2020-01-20 18:00:00', '2020-01-20 21:00:00',
                   '2020-01-21 00:00:00', '2020-01-21 03:00:00',
                   ...
                   '2020-02-28 21:00:00', '2020-02-29 00:00:00',
                   '2020-02-29 03:00:00', '2020-02-29 06:00:00',
                   '2020-02-29 09:00:00', '2020-02-29 12:00:00',
                   '2020-02-29 15:00:00', '2020-02-29 18:00:00',
                   '2020-02-29 21:00:00', '2020-03-01 00:00:00'],
                  dtype='datetime64[ns]', name='time', length=329, freq=None))

• Attributes: (5)

  units :

  kg m^{-2}

  long_name :

  snow water equivalent

  healpix_nside :

  8

  healpix_dim_name :

  cell

  original_mpas_spatial_dim :

  nCells

Show Grid Information

<uxarray.Grid>
Original Grid Type: HEALPix
Grid Dimensions:
  * n_face: 768
Grid Coordinates (Spherical):
  * face_lon: (768,)
  * face_lat: (768,)
Grid Coordinates (Cartesian):
Grid Connectivity Variables:
Grid Descriptor Variables:

uxarray.UxDataArray.uxgrid

• Dimensions:
  • n_face: 768
• Spherical Coordinates: (2)
  • face_lon
    (n_face)
    float64
    45.0 50.62 39.38 ... -50.62 -45.0

    standard_name :

    longitude

    long name :

    Longitude of the center of each face

    units :

    degrees_east

    array([  45.        ,   50.625     ,   39.375     ,   45.        ,
             56.25      ,   61.875     ,   50.625     ,   56.25      ,
             33.75      ,   39.375     ,   28.125     ,   33.75      ,
             45.        ,   50.625     ,   39.375     ,   45.        ,
             67.5       ,   73.125     ,   61.875     ,   67.5       ,
             78.75      ,   84.375     ,   73.125     ,   83.57142857,
             56.25      ,   61.875     ,   50.625     ,   57.85714286,
             70.71428571,   82.5       ,   67.5       ,   81.        ,
             22.5       ,   28.125     ,   16.875     ,   22.5       ,
             33.75      ,   39.375     ,   28.125     ,   32.14285714,
             11.25      ,   16.875     ,    5.625     ,    6.42857143,
             19.28571429,   22.5       ,    7.5       ,    9.        ,
             45.        ,   52.5       ,   37.5       ,   45.        ,
             63.        ,   78.75      ,   56.25      ,   75.        ,
             27.        ,   33.75      ,   11.25      ,   15.        ,
             45.        ,   67.5       ,   22.5       ,   45.        ,
            135.        ,  140.625     ,  129.375     ,  135.        ,
            146.25      ,  151.875     ,  140.625     ,  146.25      ,
            123.75      ,  129.375     ,  118.125     ,  123.75      ,
            135.        ,  140.625     ,  129.375     ,  135.        ,
    ...
           -135.        , -129.375     , -140.625     , -135.        ,
           -123.75      , -118.125     , -129.375     , -123.75      ,
           -146.25      , -140.625     , -151.875     , -146.25      ,
           -135.        , -129.375     , -140.625     , -135.        ,
            -45.        ,  -22.5       ,  -67.5       ,  -45.        ,
            -15.        ,  -11.25      ,  -33.75      ,  -27.        ,
            -75.        ,  -56.25      ,  -78.75      ,  -63.        ,
            -45.        ,  -37.5       ,  -52.5       ,  -45.        ,
             -9.        ,   -7.5       ,  -22.5       ,  -19.28571429,
             -6.42857143,   -5.625     ,  -16.875     ,  -11.25      ,
            -32.14285714,  -28.125     ,  -39.375     ,  -33.75      ,
            -22.5       ,  -16.875     ,  -28.125     ,  -22.5       ,
            -81.        ,  -67.5       ,  -82.5       ,  -70.71428571,
            -57.85714286,  -50.625     ,  -61.875     ,  -56.25      ,
            -83.57142857,  -73.125     ,  -84.375     ,  -78.75      ,
            -67.5       ,  -61.875     ,  -73.125     ,  -67.5       ,
            -45.        ,  -39.375     ,  -50.625     ,  -45.        ,
            -33.75      ,  -28.125     ,  -39.375     ,  -33.75      ,
            -56.25      ,  -50.625     ,  -61.875     ,  -56.25      ,
            -45.        ,  -39.375     ,  -50.625     ,  -45.        ])

  • face_lat
    (n_face)
    float64
    4.78 9.594 9.594 ... -9.594 -4.78

    standard_name :

    latitude

    long name :

    Latitude of the center of each face

    units :

    degrees_north

    array([  4.78019185,   9.59406823,   9.59406823,  14.47751219,
            14.47751219,  19.47122063,  19.47122063,  24.62431835,
            14.47751219,  19.47122063,  19.47122063,  24.62431835,
            24.62431835,  30.        ,  30.        ,  35.68533471,
            24.62431835,  30.        ,  30.        ,  35.68533471,
            35.68533471,  41.8103149 ,  41.8103149 ,  48.14120779,
            35.68533471,  41.8103149 ,  41.8103149 ,  48.14120779,
            48.14120779,  54.3409123 ,  54.3409123 ,  60.43443884,
            24.62431835,  30.        ,  30.        ,  35.68533471,
            35.68533471,  41.8103149 ,  41.8103149 ,  48.14120779,
            35.68533471,  41.8103149 ,  41.8103149 ,  48.14120779,
            48.14120779,  54.3409123 ,  54.3409123 ,  60.43443884,
            48.14120779,  54.3409123 ,  54.3409123 ,  60.43443884,
            60.43443884,  66.44353569,  66.44353569,  72.38756093,
            60.43443884,  66.44353569,  66.44353569,  72.38756093,
            72.38756093,  78.28414761,  78.28414761,  84.14973294,
             4.78019185,   9.59406823,   9.59406823,  14.47751219,
            14.47751219,  19.47122063,  19.47122063,  24.62431835,
            14.47751219,  19.47122063,  19.47122063,  24.62431835,
            24.62431835,  30.        ,  30.        ,  35.68533471,
    ...
           -35.68533471, -30.        , -30.        , -24.62431835,
           -24.62431835, -19.47122063, -19.47122063, -14.47751219,
           -24.62431835, -19.47122063, -19.47122063, -14.47751219,
           -14.47751219,  -9.59406823,  -9.59406823,  -4.78019185,
           -84.14973294, -78.28414761, -78.28414761, -72.38756093,
           -72.38756093, -66.44353569, -66.44353569, -60.43443884,
           -72.38756093, -66.44353569, -66.44353569, -60.43443884,
           -60.43443884, -54.3409123 , -54.3409123 , -48.14120779,
           -60.43443884, -54.3409123 , -54.3409123 , -48.14120779,
           -48.14120779, -41.8103149 , -41.8103149 , -35.68533471,
           -48.14120779, -41.8103149 , -41.8103149 , -35.68533471,
           -35.68533471, -30.        , -30.        , -24.62431835,
           -60.43443884, -54.3409123 , -54.3409123 , -48.14120779,
           -48.14120779, -41.8103149 , -41.8103149 , -35.68533471,
           -48.14120779, -41.8103149 , -41.8103149 , -35.68533471,
           -35.68533471, -30.        , -30.        , -24.62431835,
           -35.68533471, -30.        , -30.        , -24.62431835,
           -24.62431835, -19.47122063, -19.47122063, -14.47751219,
           -24.62431835, -19.47122063, -19.47122063, -14.47751219,
           -14.47751219,  -9.59406823,  -9.59406823,  -4.78019185])

• Cartesian Coordinates: (0)
• Connectivity: (0)
• Descriptors: (0)
• Attributes: (5)

  zoom :

  3

  n_side :

  8

  n_pix :

  768

  nest :

  True

  source_grid_spec :

  HEALPix

%%time
projection = ccrs.Robinson(central_longitude=-135.5808361)

ux_snow.mean('time').plot(
    projection=projection,
    cmap="Blues",
    features=["borders", "coastline"],
    title="Global snow: Jan-March mean",
    width=700,
)

CPU times: user 4.9 s, sys: 184 ms, total: 5.09 s
Wall time: 1.03 s

WARNING:param.GeoOverlayPlot02427: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.